Fader Seattle experience slides presentation

BCI EEG

1. Electrocorticography -Based Brain Computer Interface – The Seattle Experience

2. Rough Comparison EEG ECoG Implanted Arrays Invasiveness Low Medium High EMG Noise High Medium Low Risk Low Medium High Stability High Medium Low Spatial Resolution 1 cm 0.01 cm 0.001 cm Price $100s gazillion? buzillion ?

3. Task Control vertical position of cursor to hit the target Horizontal Speed = 1 screen width / 5.5 seconds

4. Task Interface Decoder Input (1,000 Hz): - 64 ECoG electrodes Human User Output (25 Hz): “up” or “down” magnitude Visual feedback

5. System Overview ECoG electrode placement Decoding Learning (Model) Experiment

6. ECoG Placement

7. Decoding For each user U and user action A Feature functions f(x ) Feature weights w Output is linear combination of feature functions How to choose features? How to weight features?

8. Feature Selection Rest state Action state

9. Feature Selection + Learning Training data = <signal, action state> pairs Signal = input from electrodes Action state = “performing action” or “not” Possible features = amplitude of {electrode1, electrode2, …} x {freq1, freq2, …} Rank features using autoregressive model Choose top K Weights from autoregressive model (?)

10. Experiment Offline training to learn features, weights Online development testing Online feature, weight adjustment Final round of testing

11. Interesting Observation Offline (no feedback) looks different than online (with feedback)

12. Results

13. Results (from related paper )

14. Conclusions Users can control a 1d cursor with ECoG Closed loop looks different than “open loop” Experimenting with epilepsy patients is hard